Data Limitations¶
Data bias:
When we were cleaning the data and dropping NaNs, we realized there is unequal representation because some countries appear more frequently than others leading to a skewed distribution of data.
This overrepresentation would limit the generalizability and validity of our findings and potentially leads to overlooking less-represented nations.
Additionally, this will affect the rankings and comparisons in our results because our data only represents 135 countries out of the 195 countries in the world excluding less known countries like Vanuatu, Tonga, Togo, Cabo Verde, etc.
Repetitiveness:
Our dataset on life expectancy incorporates both GDP and population data for each year, which has the potential to significantly increase the size of the dataset, posing increased challenges in terms of data management and analysis, particularly when dealing with a sizable dataset.
Additionally, this could lead to redundancy. Given that the identical GDP and population data may be replicated for each corresponding life expectancy record, this has the potential to create inefficiencies in data storage and analytical processes.
Mental health disorders could overlap:
The global mental health disorders dataset contains categories that may overlap, since mental health disorders are not mutually exclusive. For instance, people with anxiety can also have depression.
Mental health disorders may not necessarily exist in isolation, and individuals can simultaneously suffer from multiple disorders. This co-occurrence of mental health conditions presents several constraints that cause limitations when conducting analysis and interpreting results.
The original data we used, obtained from the global trends in mental health disorders dataset, consists of columns that provide details regarding the Entity, Country Code, Year, and various mental health conditions, including Schizophrenia, Bipolar Disorder, Eating Disorders, Anxiety Disorders, Drug Use Disorders, Depression, and Alcohol Use Disorders. However, it's important to note that this dataset doesn't accommodate the presence of that overlap among some of these disorders. As a result, distinguishing the individual influence of each disorder becomes challenging, and it may be problematic to identify whether the observed effects come from a single disorder or interactions among multiple disorders.
Different countries have different metrics for identifying diseases:
Different countries may use different standards and measures in the identification and diagnosis of diseases. These disparities can impact our analytical procedures and the way we interpret results, causing disparities in disease identification, which, in turn, can complicate cross-country comparisons and give rise to discrepancies in reported disease prevalence. Additionally, cultural and societal norms can further shape how individuals perceive and report symptoms, thus influencing their inclination to seek medical attention and self-reported data. For example, according to the National Institutes of Health, in the United States of America, an individual must have five depression symptoms every day, nearly all day, for at least 2 weeks while according to the National Library of Medicine, Kenyans diagnose depression with tobacco use and engagement in binge drinking.
Data Availability:
In our initial raw data, not all years seamlessly align or fit perfectly with other datasets. As a result, we found it essential to refine the scope of our analysis and narrow the years down. For instance, the unemployment analysis dataset covers the years 1991 to 2021, while other datasets, such as the life expectancy dataset, has the years 2000 to 2015. This led us to concentrate on distinct years characterized by complete and dependable data.
The HDI dataset was from Human Development Reports by the United Nations Development Programme and this dataset was created for the purpose of providing high quality international statistics that are free and accessible for all. There is no reason to question the trustworthiness of this dataset because it is an international organization that would not have personal interests to fulfill if the data results were biased.
Hanna Yukhymenko created the Urbanization dataset with the purpose of answering the question of how the percentage of urban population has developed in different continents as well as finding out what the most urban country is through analyzing both percentage and total urban population. She got this information from The World Bank organization. Even though this information is from a trustworthy organization, Hanna Yukhymenko is from Switzerland and may have been biased when creating this dataset especially because it is comparing countries.
Anjali Pant, an Engineering student from India created the unemployment dataset with the purpose of analyzing why unemployment occurs. Anjali’s country, India, has a relatively stable unemployment rate across most years hence leading us to believe that this dataset could be trustworthy. Additionally, Anjali got this data from The World Bank Organization which is also an internationally recognized organization.
Kumarra Jarshi created the Life expectancy dataset for the purpose of putting into account the immunization and human development rates in relation to life expectancy considering demographic variables, income composition and mortality rates by formulating a regression model based on mixed effects model and multiple linear regression while considering data from a period of 2000 to 2015 for all the countries. We have no reason to question the trustworthiness of this dataset because it cited the World Health Organization (WHO) as the source of the accurate data. WHO uses standardized metrics to collect data.
